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TC CHAKRABORTY: Hello, everyone.
I am TC.
I am an Earth scientist at the Pacific Northwest National Lab.
Previously I finished my PhD from Yale.
And I did a lot of work with Google Earth Engine,
particularly focused on urban heat,
as well as using it for visualizing
data, and processing data.
So I'm going to be giving some examples of sharing
and visualizing your scientific data using Google Earth Engine,
with--
you know, I'll probably go through some of the web
apps I have created, and talk about some
of the functionality.
So I mean, I don't think I need to give an introduction
to Google Earth Engine.
Probably you have been here for a while.
But it's a cloud computing platform
for a lot of spatially explicit data,
such as imagery, weather, satellites, and so on.
I think right now it's around 70 petabytes.
Or maybe this was like three months back.
So it's a lot of data that's available on the cloud
for your analysis, which makes it much easier to do
this kind of global analysis, rather than
downloading everything into your own computer,
or into a SuperCluster and doing it yourself.
So one of the first projects I worked
on using Google Earth Engine--
and this was actually for a course I took,
which introduced me to Google Earth Engine back in 2016
actually.
And what we did was we mapped the surface urban heat island
intensity at a global scale.
And this is kind of like an overview of the algorithm.
I don't want to go too deep into it.
But we basically had urban cluster boundary data,
land cover data, which allows us to get the actual land
[INAUDIBLE] information from spectral reflectance
at the surface.
And then we basically created masks
for urban pixels and non-urban pixels
within each of these clusters.
Then we processed modest land surface temperature data
and calculated the surface urban heat island.
And after doing this analysis, it
was actually my advisor who suggested like why don't we
make a web application so that people can explore the data
and interact with it?
And I had no idea how to do that.
And I spent like maybe three months trying
to understand backend stuff.
And at that point, Earth Engine did have a way
to create web applications.
But you had to have understanding
of Google Cloud and all that.
So while I was doing all that, Earth Engine
released the ability to create web applications.
And I was pretty happy about that.
So this is kind of just a screenshot of the web
application.
You can use the QR code to check it if you want.
But it's not really optimized, I would say, for desktop.
So I would switch your phone to desktop mode to check.
But I'm going to kind of just show you
some of the functionality we have in this dataset.
And all of this is like coded.
And I will share all the codes as well at the end of the talk.
I have a GitHub repo for this.
So basically, like this just shows the global urban heat
island intensity for all clusters essentially
throughout the world.
Yep, we had roughly 10,000 clusters
based on the urban cluster datasets that we used.
So yeah, you can kind of see that here.
By default, it's showing like the annual day time values.
But you can kind of switch this around,
and you can get the annual nighttime values.
Some are daytime.
Some are nighttime, and so on.
So this is just for visualization.
If you click on any of these clusters, you can also get--
that's not right.
You can get the information about the urban heat island,
as well as-- so it's basically automatically generating
these charts from the dataset.
So for instance, it shows like the annual values
for the whole cluster, as well as then the long term
changes in the annual daytime and annual nighttime
surface urban heat island, as well
as the seasonal variability of the same.
Here you can actually also export the [INAUDIBLE] images.
So if you click this, it essentially
creates like a ZIP file of all of the different layers
for that particular cluster.
However, I would generally suggest
like downloading the dataset, or accessing it on Earth Engine.
Because we made the dataset public
through Google Earth Engine.
And you can kind of--
yeah, anyway, another functionality
we had was just looking at how urban areas have
changed over time was just something
I was really interested in.
So this is like a slider.
And when you're going to slide to 2003,
it [INAUDIBLE] lower depth 2003 values.
Hello.
AUDIENCE: [INAUDIBLE] picture?
TC CHAKRABORTY: OK, yeah.
There you go.
I like to show the example here for Shanghai, because there
has been such a huge growth.
So this was 2003, urban--
so all of the values for urban heat island
are, of course, only valid if you have an urban pixel.
So it's just showing the urban pixels for 2003.
And then you can kind of go forward and see how much
expansion that has happened in the surface in just
urban areas.
And the urban land cover dataset here
is based on the European Space Agency's climate change
initiative land cover.
And it covers like 1992 to 2020, and up to, I
think, at 300 meter resolution.
Yeah, so that's one of the apps I worked on.
And it's been very unbelievably popular really.
So I think the paper that came with this app actually made it
into the most highly cited papers list
from like the Web of Science this year.
So that's nice to see.
How do I go back now?
OK, wait, that's not right.
OK, I think this is the one.
And OK, so another thing I worked on
after that was specifically looking at surface urban heat
island intensity in the US.
And the focus here was making surface urban heat island
data available at the census tract level
so that then we can combine that with socioeconomic data
to understand potential disparities in heat exposure.
So this, again, shows an algorithm
where we have an urbanized area dataset from the US Census, as
well as the census tracts themselves,
which kind of overlay those.
And here you can kind of see the surface urban heat island
intensity for some annual daytime--
for all of the 497-ish urbanized areas in the US.
And again, we created this app where
you can explore the dataset, not just the surface urban heat
island, but also like the socioeconomic data
corresponding to that.
So again, there is some similar functionality to the other one
where you can kind of switch the layers,
and show like summer day, if you want, for instance.
And similarly, on this side--
I'll just zoom in a little bit.
Let me show Chicago.
Let me see.
I'm not-- I just realized I'm not--
Oh, there you go.
All right, so yeah, here you can see the surface urban heat
island by census tracts in Chicago.
And you can also see the socioeconomic data.
Right now it's showing the percentage of white population
by each of the census tracts.
And generally, you can see like the lower percentage
of white populations are those which have like whiter,
like lighter shades.
And then the bluer shades are for a higher percentage
of white population.
You can also kind of switch this to other races considered
in the census tract data as well, as well as--
yeah, as well as like me getting them, for instance.
Generally, you can see-- and this
is pretty consistent across a lot of cities in the US,
that places where you have lower income populations
and non-white populations tend to have higher surface
urban heat island.
And again, there are some functionalities here.
You can kind of click on this, and it will give you
kind of a distribution of--
let me see.
OK, there you go.
So yeah, it's going to give you the distribution
of the urban heat island values, as well
as the median income for that census tract,
as well as the percentage of population
by different races from the census tract data.
And finally, it's going to generate a chart which
shows the correlation between the summer
daytime surface urban heat island intensity
and the income.
And generally what you will see is that across most cities
there is this negative correlation.
So higher the median income of the census tract, the lower
is the surface urban heat island intensity.
So again, the paper that came with this
was published in "Nature Communications" last year.
And again, this code I have also made public.
So you can kind of play around with it for something similar,
if you're working on some similar project.
Another project-- let me go back.
All right, so the next project is very
different from the other two.
And this is more related to what I was working on during my PhD.
Like this creates a biased corrected dataset for shortwave
radiation and diffused radiation.
I use some machine learning algorithms
to fix the dataset in a global re-analysis,
based on observations.
So for instance, right now I can just click on this
and show you--
this is a very simple script.
It just kind of allows you to explore the long term time
series for these different datasets.
So if I can just click on any of these points,
it's going to create a chart showing how this changed.
This is total shortwave radiation
coming from the sun, how that changed from 1980 to 2019.
And it's also going to divide that up
into two components that is the diffused radiation, which
is the amount of the shortwave radiation whose
direction changes due to scattering by aerosols
and clouds.
And the other is the direct beam radiation.
This is the radiation that just comes directly from the sun
without any interactions with particles.
Yeah, and again, you can kind of--
the same.
I add this feature everywhere because I have the code for it.
Yeah, so this is another one.
I'm going to talk about one last one probably,
and then maybe you can take questions.
I don't know what questions [INAUDIBLE]..
Another study I worked on recently,
this was done in collaboration with the Nature Conservancy.
So OK, let's do some background.
So there has been a lot of talk about more vegetation
in urban areas, because that has multiple co-benefits,
as well as increased heat mitigation capacities.
But one question we had was how much vegetation
is actually possible in urban areas, given that there
is urban densification.
There's not enough space, especially
in poorer neighborhoods to have vegetation if you want.
So what we did was we actually calculated
the gap in tree cover between highest quartile
and lowest quartile by income census block groups
across cities in California.
And then we kind of tried to find the space required
to close this tree gap based on land cover datasets and 1 meter
resolution urban vegetation datasets.
And finally, we also did something similar,
but to close the surface urban heat island gap.
Generally, you will find that there is not
enough space, especially in the middle of the city,
to fully close the gap.
Because a lot of these census tracts are almost entirely
built up.
You have a bunch of them like filled
with parking lots where there is no space for urban vegetation.
Anyway, so we also created an app for this.
I didn't make this app.
This was one of my co-authors.
But basically, it allows you to just explore the dataset.
Like there's a bunch of variables.
And this dataset is also available on the GitHub repo,
which is linked through the app.
So it shows like current tree cover for--
let's see.
Let's go somewhere.
All right, so let's see.
So this shows like current tree cover, for instance,
for this city.
And you can kind of switch that around to show
current urban heat island, and surface urban heat island.
Yeah, and kind of so on.
And, of course, here you can see that lower urban heat island
towards the coast, which is usually
like the effect of the sea breeze.
And that's pretty well known.
So yeah, that's another app.
There are a bunch of others I have,
which I worked on as well.
But I'm going to also give you some kind of examples of these.
There are a lot of other apps from other groups
which are really good, and have a lot of more functionality as
well.
And some of these are actually available on Earth Engine.
So if you go to the Code Editor--
so if you go to the Code Editor, there
are some scripts available for a lot of these apps.
[INAUDIBLE]
Oh, I think-- let me see.
I have to expand one of the folders to get this.
Oh, there you go, the population explorer app.
So, for instance, this one, I really
like this app, because it basically
allows you to look at population distributions around the globe.
And you can kind of click on them.
And it will basically generate a chart for total population
by country.
And you can keep adding a few of these charts.
I don't remember exactly how many
you can add till it starts replacing, but a bunch of them.
And it's basically going to calculate-- yeah, see,
you can compare populations.
And all of this is already available through the Code
Editor, like in some of these scripts.
So for the scripts that I talked about today, like apps,
I actually have made all of them public on GitHub.
It's not well commented, just letting you know.
I might update it in the future, but I
did put them up there if you want to play around with it.
Yeah, thank you.
And let me know if you have any questions.
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